Source: Our World In Data

(Source: http://ourworldindata.org)

rm(list=ls())

db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
df <- dbGetQuery(db,"select * from OWID where location ='United States'")
df$date <- as.Date(df$date)

df <- df %>% filter(date >="2020-12-25") %>% 
  select(date,total_vaccinations:new_vaccinations_smoothed_per_million,population)

Various Vaccine Metrics

p1 <- ggplot(df) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels = comma) +
  labs(title="US total vaccinations Doses by Date",
       subtitle ="Total number of COVID-19 vaccination doses administered" )
ggplotly(p1)
p2 <- ggplot(df) + geom_line(aes(x=date,y=people_vaccinated)) +
  scale_y_continuous(labels = comma) +
  labs(title="US Total People Vaccinated To Date",
       subtitle = "Total number of people who received at least one vaccine dose")
ggplotly(p2)
p3 <- ggplot(df) + geom_line(aes(x=date,y=people_fully_vaccinated)) +
  scale_y_continuous(labels = comma) +
  labs(title="US Total People Fully  Vaccinated To Date",
       subtitle = "Total number of people who received all doses prescribed by the vaccination protocol")
ggplotly(p3)
p4 <- ggplot(df) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels = comma) +
  labs(title="US Daily People  Vaccinated by Date",
       subtitle = "New COVID-19 vaccination doses administered\n (only calculated for consecutive days)")
ggplotly(p4)

“New COVID-19 vaccination doses administered (7-day smoothed). For countries that don’t report vaccination data on a daily basis, we assume that vaccination changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window”

p5 <- ggplot(df) + geom_line(aes(x=date,y=new_vaccinations_smoothed)) +
  scale_y_continuous(labels = comma) +
  labs(title="US Daily People  Vaccinated by Date",y="Daily Vaccinations (Smooth)")
ggplotly(p5)
p6 <- ggplot(df) + geom_line(aes(x=date,y=total_vaccinations_per_hundred)) +
  scale_y_continuous(labels = comma) +
  labs(title="US Total Vaccinations Per Hundred",y="Daily Vaccinations (Per 100 People)",
       subtitle = "Total number of COVID-19 vaccination doses administered per 100 people in the total population")
ggplotly(p6)
p7 <- ggplot(df) + geom_line(aes(x=date,y=people_vaccinated_per_hundred)) +
  scale_y_continuous(labels = comma) +
  labs(title="US People Vaccinated Per Hundred",y="People Vaccinated (per 100)",
       subtitle = "Total number of people who received at least one vaccine dose per 100 people in the total population")
ggplotly(p7)
p8 <- ggplot(df) + geom_line(aes(x=date,y=people_fully_vaccinated_per_hundred)) +
  scale_y_continuous(labels = comma) +
  labs(title="US People Fully Vaccinated Per Hundred",y="People Vaccinated (per 100)",
       subtitle = "Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population")
ggplotly(p8)
p9 <- ggplot(df) + geom_line(aes(x=date,y=new_vaccinations_smoothed_per_million)) +
  scale_y_continuous(labels = comma) +
  labs(title="New Vaccinations Smoothed per Million",y="Vaccinations",
       subtitle = "New COVID-19 vaccination doses administered (7-day smoothed) per 1,000,000 people in the total population")
ggplotly(p9)